Hybrid Virtual- and Field Work-based Service Learning with Green Information Technology and Systems Projects
Bibliographic record
Abstract
Traditional engineering service learning (SL) projects can be classified as: 1) collaborations with a community group or non-profit organization to provide specific engineering around a community need, or 2) an internship-like experience with industry to address work requested by a client. The limitation of both traditional SL approaches is that they do not prepare students to implement unprescribed projects. In contrast, here students chose both the project and the partner for a self-directed engineering SL experience. This paper presents the findings of this novel pedagogical exercise in which students acted as change agents for industry by implementing unsolicited energy conservation measures (ECMs) focused on green information technology and systems (IT/S), in order to improve organizations’ environmental and economic performance. The hybrid SL projects had both ‘virtual’ and ‘real’ (field-work) SL components. For the virtual component, student teams developed and published on-line, open-source ECM calculators. For the field-work component, the teams self-selected industry clients and performed IT/S energy audits. Applicable ECMs were then selected and tailored, forming the basis of recommendations to the organizations. Results demonstrate the effectiveness of such hybrid engineering SL projects.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".